THE EFFECT OF CORPORATE DIVERSIFICATION ON CAPITAL
STRUCTURE OF FIRMS LISTED IN THE NAIROBI SECURITIES
EXCHANGE
GRACE WAIRIMU
A RESEARCH PROJECT PRESENTED IN PARTIAL FULFILMENT OF THE
REQUIREMENTS FOR THE AWARD OF MASTERS OF BUSINESS
ADMINISTRATION DEGREE, SCHOOL OF BUSINESS-UNIVERSITY OF
NAIROBI.
OCTOBER 2015
DECLARATION
This Research Project is my original work and has not been submitted for examination in
any other university. The work referred has been acknowledged by:
Sign:
Date:
Grace Wairimu
D61/64432 /2013
Research Project has been submitted for examination with our approval as the:
Supervisor
Sign........................
Date...................................
DR. DUNCAN ELLY OCHIENG PhD, CIFA
Lecturer, School of Business
University of Nairobi
ii
ACKNOWLEDGMENT
I would like to acknowledge God the Almighty for enabling me complete this project. I
am thankful for the spirit of understanding, diligence, patience and good health as I
undertook this project.
I extend my sincere gratitude to my supervisor, Dr. Duncan Elly Ochieng, for offering his
expertise and unrelenting guidance throughout the project. Indeed it has been a long
journey, with a successful ending. I have gained in terms of intellectual knowledge and
skills of facing the world with an open mind.
Lastly, I wish to thank all my friends and colleagues for their words of encouragement
and support throughout the project. It is my prayer that God will reward you accordingly.
iii
DEDICATION
I dedicate this project to my parents for their passionate love for education, my uncle
John Mburu who has relentlessly put enormous effort to see me succeed in life and my
younger brother Chris Kigochi. It is my commitment to inspire my siblings, cousins and
friends to achieve even more in their endeavours.
iv
ABSTRACT
Firms have to constantly review their strategies for them to remain competitive in the
changing environment. Diversification is thus almost inevitable for firms with new
entrants into the market threatening existing firms. This study thus sought to find out the
effect of corporate diversification on a firm`s financial decisions. There have been mixed
propositions as to whether diversification leads to value addition of a firm or it destroys a
firm`s worth. The researcher targeted 44 non-financial firms, but the actual study focused
on 35 firms which represented 80 percent of the target population for the period 2010 to
2014. The data was mainly from annual financial reports of respective companies. The
annual reports were obtained from the firms’ websites, Capital Markets Authority and
other relevant publications. Multiple regression and bivariate correlation were used to
analyze the data. Diversification
was
measured
using
specialization
ratio,
calculated as a ratio of annual revenue from the core segment of a firm to its total
annual revenue. The adjusted R2 value was 0.07, which meant that only 7 percent of
variations in leverage for listed firms were explained by the variations in the model’s
independent variable and control variables. The study found that diversification and
tangibility of assets had a weak positive relationship with leverage. The relationship
between leverage and diversification, and leverage and tangibility was not statistically
significant. Firm size and profitability had statistically significant relationship with
leverage. Firm size had a weak positive association with leverage, whereas profitability
had a weak negative association with leverage. In summary, corporate diversification had
a weak positive relationship with leverage. Further research should be carried out on the
effect of related and unrelated diversification on a firm`s capital structure. A study on
other factors affecting capital structure of a firm should also be carried out.
v
TABLE OF CONTENTS
DECLARATION............................................................................................................... ii
ACKNOWLEDGMENT ................................................................................................. iii
DEDICATION.................................................................................................................. iv
ABSTRACT ....................................................................................................................... v
LIST OF TABLES ........................................................................................................... ix
LIST OF ABBREVIATIONS .......................................................................................... x
CHAPTER ONE: INTRODUCTION ............................................................................. 1
1.1 Background to the Study............................................................................................... 1
1.1.1 Corporate Diversification..................................................................................... 2
1.1.2 Capital Structure .................................................................................................. 3
1.1.3 Corporate Diversification and Capital Structure.................................................. 4
1.1.4 Nairobi Securities Exchange ................................................................................ 5
1.2 Research Problem ......................................................................................................... 6
1.3 Research Objectives ...................................................................................................... 8
1.4 Value of the Study ........................................................................................................ 8
CHAPTER TWO: LITERATURE REVIEW ................................................................ 9
2.1 Introduction ................................................................................................................... 9
2.2 Theoretical Review ....................................................................................................... 9
2.2.1 The Co-insurance Effect ...................................................................................... 9
2.2.2 Resource Based View ........................................................................................ 10
2.2.3 The Transaction Cost Argument ........................................................................ 11
2.3 Determinants of Capital Structure .............................................................................. 11
vi
2.3.1 Diversification.................................................................................................... 12
2.3.2 Firm Size ............................................................................................................ 12
2.3.3 Profitability ........................................................................................................ 13
2.3.4 Tangible Assets .................................................................................................. 14
2.4 Review of Empirical Evidence ................................................................................... 15
2.5 Summary of the Literature Review ............................................................................. 18
CHAPTER THREE: RESEARCH METHODOLOGY ............................................. 19
3.1 Introduction ................................................................................................................. 19
3.2 Research Design.......................................................................................................... 19
3.3 Population ................................................................................................................... 19
3.4 Data Collection ........................................................................................................... 20
3.5 Data Analysis .............................................................................................................. 20
3.5.1 Model Specification and Operationalization of Variables ................................. 20
3.5.2 Tests of Significance .......................................................................................... 22
CHAPTER FOUR: DATA ANALYSIS, RESULTS AND DISCUSSION ................ 23
4.1 Introduction ................................................................................................................. 23
4.2 Descriptive Statistics................................................................................................... 23
4.2.1 Leverage ............................................................................................................. 24
4.2.2 Diversification.................................................................................................... 24
4.2.3 Size..................................................................................................................... 25
4.2.4 Profitability ........................................................................................................ 26
4.2.5 Tangibility .......................................................................................................... 26
vii
4.3 Diagnostics Tests ........................................................................................................ 27
4.4 Relationship between Study Variables ....................................................................... 31
4.5 Corporate Diversification and Capital Structure......................................................... 33
CHAPTER FIVE: SUMMARY, CONCLUSIONS AND RECOMMENDATIONS 37
5.1 Introduction ................................................................................................................. 37
5.2 Summary of Findings.................................................................................................. 37
5.3 Conclusions ................................................................................................................. 40
5.4 Recommendations ....................................................................................................... 41
5.5 Limitations of the Study.............................................................................................. 42
5.6 Suggestions for further Research ................................................................................ 43
REFERENCES ................................................................................................................ 44
APPENDICES: ................................................................................................................ 51
Appendix 1: Data Collection Sheet................................................................................... 51
Appendix 2: List of Listed Non- Financial Firms as at Dec 2014 .................................... 56
viii
LIST OF TABLES
Table 3.1: Operationalization of Study Variables..................................................................22
Table 4.1: Descriptive Statistics on Leverage, Diversification, Size, Profitability and
Tangibility ............................................................................................................23
Table 4.2a: Multicollinearity Test Profitability, Tangibility and Size with Diversification ..27
Table 4.2b: Multicollinearity Test Profitability, Tangibility and Diversification with Size .28
Table 4.2c: Multicollinearity Test Profitability, Tangibility and Diversification with
Tangibility ............................................................................................................28
Table 4.2d : Multicollinearity Test Size, Diversification and Tangibility with
Profitability ..........................................................................................................29
Table 4.3: Durbin Watson`s Test: Model Summary ..............................................................29
Table 4.4a: Tests of Normality :Case Processing Summary..................................................30
Table 4.4b: Tests of Normality ..............................................................................................30
Table 4.5: Correlation Matrix on Relationship between Study Variables .............................31
Table 4.6a: Regression statistics: Model Summary ...............................................................34
Table 4.6b : ANOVAa ...........................................................................................................34
Table 4.6c: Regression Coefficientsa ....................................................................................34
ix
LIST OF ABBREVIATIONS
CDS
-
Central Depository System
CMA -
Capital market Authority
DIVE -
Diversification
LEVE -
Leverage
NDTS -
Non-Debt Tax Shields
NSE
-
Nairobi Securities Exchange
POT
-
Pecking Order Theory
PROF -
Profitability
R&D -
Research and development
SPSS -
Statistical Package for Social Science
TANG -
Tangible
US
-
United States of America
VIF
-
Variance Inflation Factor
x
CHAPTER ONE:
INTRODUCTION
1.1 Background to the Study
To understand how firms in developing countries finance their operations, it is
necessary to examine the determinants of their financing or capital structure decisions.
Company financing decisions involve a wide range of policy issues. At the macro level,
they have implications for capital market development, interest rate and security price
determination, and regulation. At the micro level, such decisions affect capital structure,
corporate governance and company development (Green, Murinde and Suppakitjarak,
2002).
Diversification has been studied mainly in relation to firm value. A majority of authors
argue that diversified firms need greater leverage to maximize firm value (Singh et.al.,
2003).There are vices that are associated with diversification with the risk of
conglomerates value threatened with discounting as opposed to firms that stick to their
primary core activities (Servaes,1996). The decline in value is associated with an increase
in the number of segments generating agency problems (Hyland and Diltz, 2002).
Conversely, diversification offers firms financing and investment advantages. Diversified
firms are able to transfer scarce capital among divisions to finance some projects at the
expense of others (Matsusaka & Nanda, 2002).
1
1.1.1
Corporate Diversification
Corporate diversification is the process by which a firm expands from its core
business into other product markets (Gluck, 1985).Corporate diversification can be
approached from different perspectives. Hill (1985) in expounding market power
perspective suggests that diversified firms will thrive at the expense of non-diversified
firms not because they are any more efficient but because they have access to
conglomerate power.
The agency view emphasizes the way a firm`s manager may reap at the expense of the
shareholder. Ueng and Wells (2001) provide empirical evidences that the combination of
lower incentive ratio and more diversified acquisition instead of the focused
acquisition
produces the lowest return. Thus, their study supports the assumption that
manager of an acquiring firm may pursue personal wealth maximization rather than
shareholder wealth maximization. Managers who pursue their wealth maximization
might also knowingly undertake value-decreasing investment. Resource view on the
other hand argues that firms diversify in response to excess capacity in productive factors
called resources (Penrose, 1959). They include factors the firm has purchased in the
market, services the firm has created from those factors and special knowledge the firm
has accumulated over time.
2
Corporate diversification is measured using the specialization ratio which is defined as
the ratio of the firm`s annual revenues from its largest discrete, product-market activity to
its total revenues (Rumelt, 1974). In the diversification literature, specialization ratio has
gained popularity in measuring diversification due to its ease in calculation and
understanding. Logic of the specialization ratio method reflects the importance of the
firm`s core product market to that of the rest of the firm (Rumelt,1974,1982).
In Rumelt`s study, he classifies firms into three groups; single product groups with SR
≥0.95; moderately diversified firms with SR values between 0.95<SR ≤0.7, this group
includes dominant relatedly and unrelatedly diversified firms; the highly diversified firms
have SR <0.7 and includes conglomerates relatedly constrained and relatedly – linked
firms. Thus, firms are moderately diversified if their sales from the dominant business lie
between 95% and 70%, whilst highly diversified firms sales from dominant business are
below 70%.
1.1.2
Capital Structure
Capital structure basically refers to a firm`s financial framework. Predominantly, it is the
mix of debt and equity capital maintained by a firm. It is also seen as a mixture of a long
term sources of funds and equity shares including reserves and surpluses of an enterprise
(Booth, Aivazian, Demirguc-Kunt & Maksimovic, 2001). Capital structure is viewed as
the composition of all the securities the firm issues in order to finance its operations. It is
thus the way a firm combines equity and debt to gain the maximum value. The value of a
firm is therefore defined as the market value of debt plus the market value of equity (Ross
et al., 2009).
3
The proportion of debt to equity is a strategic choice of corporate managers. An optimal
capital structure is usually defined as one that will minimize a firm`s cost of capital,
while maximizing shareholder`s wealth (Niu, 2008). Capital structure decision is thus
vital, as any misjudgment regarding the making of financial decision of any activity
would lead to financial distress, liquidation or bankruptcy.
1.1.3
Corporate Diversification and Capital Structure
Corporate diversification is still a concept not largely explored by many authors. Weston
(1970) proposes that resources could be allocated more efficiently within an organisation
than in capital markets, thus diversified firms would be more efficient than nondiversified firms. Rumelt (1982) found out that conglomerates have significantly lower
profitability in comparison to more focused firms.
Lewellen (1971) however argues that combining businesses with imperfectly correlated
streams of cashflows provides a coinsurance effect which creates more debt capacity.
Thus, while diversification may destroy value and profitability, its effect may be partially
offset by an increased debt capacity and resulting tax shields. Li and Li (1996)
reemphasize the concept by arguing “the combination of diversification with low
leverage leads to overinvestment.”
Matsusaka (2001) refers to diversification as the process by which firms search for new
uses of their organizational capabilities. A number of firms in the NSE have diversified;
for example firms in the telecommunication industry have diversified to include some
banking
aspects;
some
firms
in
the
banking
industry
have
incorporated
telecommunication services and investment banking; with some agricultural firms and
investment firms having incorporated energy production in their businesses.
4
This serves to show the appreciation of corporate diversification amongst firms listed in
the NSE. By analyzing balance sheets of firms listed in the NSE, most firms have both
equity and debt in their capital structure. A large number of the firms have more equity
than debt in their capital structure (Nairobi Security Exchange Handbook, 2013).
1.1.4
Nairobi Securities Exchange
The Nairobi Securities Exchange, which was formed in 1954 as a voluntary organization
of stockbrokers, is now one of the most active capital markets in Africa. The market has
experienced both growth and instability in the past. In 1972 for example, growth of the
market halted with oil crisis introducing inflationary strain to the economy. The
government of Kenya realized the need to design and implement policy reforms to foster
sustainable economic development (NSE Market Fact File, 2008).
Currently there are 61 companies listed on the NSE under its 11 segments. The segments
include Banking (12), Agricultural (7), Commercial and Services (9), Telecommunication
and Technology (1), Automobiles and Accessories (4), Insurance (6), Investment (3),
manufacturing and Allied (9),Construction and Allied (5) and Energy and Petroleum
(5).The NSE is the principal securities exchange of Kenya and it is licensed and
regulated by the Capital Markets authority (CMA). Firms in the NSE have mainly used
product and geographic diversification strategies to sustain themselves in the existing
market.
5
1.2 Research Problem
Firms have to constantly review their strategies for them to remain competitive in the
changing environment. Diversification is thus almost inevitable for firms; with new
entrants into the market threatening existing firms. There are two types of diversification
related and unrelated which have different effects on the capital structure of firms. A
related diversification strategy is associated with lower debt usage and has a negative
influence on leverage. Unrelated diversity is associated with high debt usage and is
positively related with debt. The effect of diversification strategy thus faces mixed
reactions from scholars. Rushin (2006) argues that it is unclear if diversification adds
value to an organisation as opposed to a firm that adopts a more focused strategy.
Related diversifiers exemplify higher results in their economic growth. Lubatkin (1987)
has however suggested that single product models or unrelated diversification can be
more advantagious than related diversification. Scholars in support of unrelated
diversification argue that there exists imperfect correlation between the cash flows of
divisions or projects reducing the risk of default and increasing the firm`s collateral
resulting in greater access to credit (Lewellen, 1971). This effect is called “crosspledging” by Tirole (2006), meaning that firms can use income they receive from a
successful project as collateral for the financing of another provided they are independent
projects.
6
Most firms that are listed in Nairobi securities exchange have shown a keen interest on
diversification of unrelated businesses of their investment in order to boost their returns.
Some listed firms have diversified their portfolios into new asset classes to boost returns
and reduce risk; the asset classes they are turning to are often illiquid (Ngugi, 2005).
Mwangi (2013) found that agent banking was highly useful as a diversifying strategy
among listed NSE banks, as banks used agent banks to expand geographical coverage and
promote their products and services. Koech (2013) found that there was a very weak
correlation between corporate diversification returns and leverage for the listed firms at
the Nairobi Securities Exchange. As such there are mixed reactions as to the effect of
corporate diversification on capital structure amongst listed firms at the NSE.
Local studies have been carried out attempting to investigate the effect of diversification
on various firms. According to Ngugi (2008) the main determinants of capital structure
behaviour of firms listed in the NSE are information asymmetries, non-debt tax shields
and local capital markets infrastructure. Mwindi (2003) did an analysis of the implication
of unrelated diversification strategy by the major oil companies in Kenya and found out
that diversification concept as used in most retail networks of oil companies lend itself
more to enhancing customer satisfaction. As observed, scanty research has been carried
out investigating the relationship between corporate diversification and capital structure.
The study thus seeks to answer the question; what is the relationship between corporate
diversification and capital structure of firms listed in the NSE?
7
1.3 Research Objectives
The objective of this study was to establish the effect of corporate diversification on
capital structure of firms listed at the Nairobi Securities Exchange.
1.4 Value of the Study
The study sought to carry out an independent analysis between corporate diversification
and capital structure of firms. This is beneficial to managers in formulation and
implementation of corporate strategies that seek to have the optimal capital structure.
Researchers are to benefit from additional knowledge generated by the study. It has
generated factual information and data which can be used by scholars to form the basis of
their study in furthering the research on corporate diversification and capital structure.
The study also supports the already existing theories on corporate diversification and
capital structure.
Capital market regulators have gained information on how manager`s strategies affect a
firm`s financing. The regulators are thus able to value firms favorably for both the
investors and household savers. They are also able to come up with policies for full
disclosure of private information so as to avoid misvaluation problem.
Financial analysts engage in lots of forecasting and stock recommendations. They gain
useful insights from this research on the various determinants of capital structure and thus
able to advise investors accordingly. They are also able to analyse the effects of various
managers` strategies and uncover any ulterior motive unfavorable to investors.
8
CHAPTER TWO:
LITERATURE REVIEW
2.1 Introduction
This chapter discusses the work of other scholars and researchers relating to corporate
diversification effect on capital structure. Focus is on the theories of diversification and
how it consequently affects capital structure and the theoretical foundations that inform
the current study.
2.2 Theoretical Review
There are three theories that try to support the effect of corporate diversification on
capital structure choices. They include the Coinsurance Effect, Resource Based View and
the Transaction Cost Approach.
2.2.1 The Co-insurance Effect
Lewellen (1971) states that combining businesses with imperfectly correlated cash flows
provides a reduction in operating risk thereby enhancing corporate debt capacity. Fatemi
(1984) also provides evidence on the risk-reduction effect of international diversification.
By comparing a portfolio of multinational firms with a portfolio of purely domestic firms,
he finds that corporate international diversification reduces systematic risk.
Singh (2003) argues that, if the co-insurance effect enhances debt capacity and results in
increased debt usage for product-diversified firms, it would be reasonable to expect a
similar impact for geographically diversified firms, when geographic diversification
occurs across political boundaries with imperfectly correlated cash flow streams.
9
Apostu (2010) argues that diversification provides coinsurance effect which has positive
relation with firm debt capacity more especially because it reduces the risk of fluctuations
amongst a firm`s revenues and profits. Firms using unrelated diversification strategy have
greater lack of correlation and thus experience intense coinsurance effect, and more debt
capacity.
2.2.2 Resource Based View
Resource based theory takes a firm as a collection of sticky and imperfectly imitable
resources or capabilities which enable it to successfully compete against other firms
(Barney 1991).Resource based firm theory is a strategic theory about how a firm can
exploit the resources to achieve its economic goals or sustainable competence advantages
over its rivals. As such provided a firm has idle capacity which it can utilize to make
more money, such a firm will diversify.
Silverman (1999) find that firms with broad resource bases tend to diversify, and they
tend to diversify into industry that have similar R&D, advertising and capital expenditure
intensities to those of the firm`s existing businesses, and firms tend to enter the markets
where the resources requirements match their resource capabilities. Therefore, resource
based theory helps us understand why firms expand, and why a firm chooses a specific
diversification strategy over another.
10
2.2.3 The Transaction Cost Argument
The transaction cost approach deals with the governance of contractual relations in
transactions between two parties (Williamson, 1988). This theory shows the relationship
existent between type of diversification chosen by a firm and its likely choice of capital.
Firms using unrelated diversification strategy tend to have a capital structure that has
more debt as opposed to firms applying related diversification. One explanation is the use
of general assets by firms employing unrelated diversification strategy which retain their
value in case of liquidation.
Apostu (2010) suggests that the type of diversification adopted by a firm depends on the
nature of unutilized resources that leads firms to diversify. Since the type of assets
employed by a firm influence financial decisions it is possible to establish a relationship
between capital structure and the diversification strategy using transactions costs theory.
Most scholars argue of the existence of a positive relationship between leverage and firm
diversification strategy.
2.3 Determinants of Capital Structure
Empirical studies on capital structure determinants build on a list of variables likely to
affect capital structure choices which include; diversification, firm size, profitability,
tangible assets and non-debt tax shields.
11
2.3.1 Diversification
Diversification can improve debt capacity, reduce the chances of bankruptcy by going
into new product/ markets (Lewellen, 1971), and improve asset deployment and
profitability. Skills developed in one business transferred to other businesses, can
increase labor and capital productivity. Raphael and Livnat (1988) using market based
risk measures found that firm`s trade off the reduction in operating risk due to
diversification with increased financial leverage, and thus the systematic risk remains the
same. It documents that firms reduce their operating risk by diversification and increase
financial leverage to take advantage of tax benefits.
A diversified firm can transfer funds from a cash surplus unit to a cash deficit unit
without taxes or transaction costs (Bhide, 1993). As a result of the coinsurance effect,
resource view and transaction cost effect, diversification becomes attractive to investors
and debt capacity is thus significantly enhanced. Low and Chen (2004) in their study,
emphasized that product diversification is positively related to financial leverage,
indicating that such diversification allows corporations to reduce their risks enabling
them to carry higher debt levels.
2.3.2 Firm Size
Firm size defines the extent to which firms can access credit markets to get loans. Firm
size can be measured using total assets, total assets to book value, average level of total
assets and average level of sales (Ferri and Jones, 1979).
12
If there are returns to scale in the costs of issuing securities, larger firms might change
their leverage more readily than smaller firms. Larger
investment
projects
on
firms
can
diversify
their
a broader basis thus their financial distress risk can be
considered to be lower. The trade off theory suggests a positive relation between size and
leverage (Sbeiti, 2010).
Titman and Wessels (1988) have opined that small size is likely to worsen the
information asymmetry between the small firm shareholders, managers and potential
capital lenders. As a result, the cost of debt may be higher for SME`s than for large firms.
Ang, et al. (1982) argue that bankruptcy costs are relatively higher for small companies,
because large firms show more stability and hold more diversified portfolios of assets.
Kaijage and Elly (2014) conclude the existence a positive relationship between firm size
and total and long-term debt on one side, and a negative relationship between firm size
and short-term debt on the other.
2.3.3 Profitability
The relationship between firm profitability and capital structure can be explained by the
pecking order theory (POT), which holds that firms prefer internal sources of finance to
external sources. The order of the preference is from the one that is least sensitive (and
least risky) to the one that is most sensitive (and most risky) that arise because of
asymmetric information between corporate insiders and less well-informed market
participants (Myers, 1984). By this token, profitable firms with access to retained profits
can rely on them as opposed to depending on outside sources (debt).
13
Profitability is measured as the ratio of earnings before tax to total assets. Rajan and
Zingales (1995) use earnings before interest, taxes, and depreciation while Booth et al
(2001) use the return on assets defined by the earnings before tax. The relationship
between leverage and profitability is indeterminate.
Profitable firms have lower expected costs of financial distress and thus have easy access
to the debt market. Kaijage and Elly (2014) argue that contrary to the norm, profitable
small and medium enterprises tend to use retentions as the principle source of funds as
opposed to debt. This proposition contradicts the conventional theory that the cost of debt
is usually lower than the cost of equity. A possible explanation is the risk associated with
small firms that makes debt capital costlier.
2.3.4 Tangible Assets
This is viewed as the palpability of assets in the balance sheet and measures the
proportion of firm’s fixed assets. The type of assets owned by a firm affects its
capital structure choice (Chang, Lee & Lee, 2008). Tangible assets, which retain high
liquidation value, serve as debt security. However, if tangible assets are illiquid,
firms have a lower debt capacity. Frank and Goyal (2005), measure tangibility as
fixed assets to total assets and expect a positive relationship as firms with a
greater percentage of total assets composed of tangible assets are more likely to have a
higher capacity to raise debt.
14
Jensen and Meckling (1976) argue that shareholders of levered companies are inclined to
overinvest, which intensifies the classical conflict of interests between stockholders and
debt holders. If a firm has a high proportion of long term physical assets, leverage can be
secured against these assets. However, in this situation the corporate manager would be
restricted to using debt funds for specific projects. Harris and Raviv (1991) conclude that
high tangibility of assets may increase the liquidation value of a firm and improves the
guarantee of repayment, reducing the risk to debtors.
2.4 Review of Empirical Evidence
Rumelt (1974) investigated the effect of diversification on capital structure for
a
sample of 249 US firms. He found out that for firms developing a strategy of unrelated
diversification had the highest debt ratio. Barton and Gordon (1988) propose that
corporate strategy perspective may provide a behavioral basis for understanding the
capital structure of large US firms. Their study also revealed that firms developing a
strategy of unrelated diversification had the highest debt ratio.
Kochhar and Hitt (1998) also explored the linkage between the characteristics of a firm’s
diversification strategy and its capital structure. They find that equity financing is
preferred for related diversification and debt financing for unrelated diversification. Their
explanation is that related diversification introduces more specific assets whereas
unrelated diversification adds assets less specific to the firm.
15
Alonso (2003) studied the effect of diversification strategy on firm capital structure using
a panel data analysis for a sample of 480 Spanish manufacturing firms during the
period 1991-1994. Using four alternative measures of capital structure and two
different proxies of diversification strategies (the Herfindahl and the Entropy index of
total product diversification) and after controlling for firm characteristics such as firm
size, intangible assets and firm profitability, he finds no significant relationship between
capital structure and the degree of firm diversification.
Nyangoro (2003) studied the determinants of capital structure. He used conditional
quantile regression in analyzing the distributional differences of debt ratios across firms
in different quantiles. He concluded that some variables affect capital structure, for
example, the increase in size of the firm leads to firms shifting from long term to short
term debt, while asset tangibility leads to a shift from short term to long term debt by the
firm. The effect of tax on capital structure is only significant at
lower quantiles and
only for total debt ratios. Firms at higher debt quantiles use non-debt tax shields
other than tax rate to determine their capital structure.
La Rocca et.al. (2009) extended prior analyses on financial policy and diversification by
examining the relationship between capital structure and diversification over a period of
twenty seven years. Their sample consisted of a panel made up of 180 Italian
firms (76 listed) evaluated in the period from 1980 to 2006.
16
Using a target adjusted model estimated by the Generalized Method of Moments (GMM)
approach, they show that total diversification is negatively related to debt ratios.
Furthermore, their analysis indicated that the degree of relatedness between business
segments is important in the relationship between diversification and capital structure.
They find that a related-diversification strategy, which is based on business synergies and
resource sharing, has a negative influence on leverage. By contrast, unrelated diversity,
which is based on financial synergies, has a positive effect on debt. In
addition they
find that the diversification structure significantly influenced the speed at which firms
adjusted their leverage ratios.
Qureshi (2012) carried out a study to investigate the nature of relationship existent
between diversification, capital structure and profitability in Pakistan. The study was on a
sample of 74 companies listed in the Karachi Stock Exchange from 2000 to 2009. Two
dimensions of diversification were considered product and geographic diversification.
The results supported the coinsurance and the transaction cost theory; firms having
product and geographic diversification were found to have greater amount of debt as
compared to the non-diversified firms. Product diversification positively affected
profitability, with the diversified firms earning more on average.
17
Nyanamba, Nyangweso and Omari, (2013) did a research on the factors that determine
the capital structure of micro-enterprises. Their research targeted 200 active microenterprises within Kisii town. Using simple random design they identified the 80 (40%)
micro enterprises for study. They found out that some determinants of capital structure
seemed to be more significant as compared to others. The greatest determinants identified
were: access to capital markets, size of the business, and profitability of the business and
lender’s attitude towards the firm.
2.5 Summary of the Literature Review
Authors such as Rumelt (1974), Barton and Gordon (1988) conclude that debt capacity is
highest in unrelated diversified firms. Kochhar and Hitt (1998) conclude that equity
financing is preferred for related diversification and debt financing for unrelated
diversification. Alonso (2003), on the other hand, finds no significant relationship
between capital structure and the degree of firm diversification. This shows the
inconclusive nature of the effect of diversification on capital structure indicating the
existence of a knowledge gap.
Many of the studies that have been carried out are from developed countries whose
institutions effect on diversification differs with those of developing countries. There is
no conclusive information as to which is the optimal capital mix a firm should adopt.
There is also no published information in Kenya on the effect of diversification on capital
structure of listed firms in Kenya. This study seeks to address the existent gap by trying
to establish the effect of diversification on the growth of listed companies in the NSE
18
CHAPTER THREE:
RESEARCH METHODOLOGY
3.1 Introduction
This chapter discusses the research design and the methodology of the study; it highlights
a full description of the research design, the research variables and provides a broad view
of the description and selection of the population. The research instruments, data
collection techniques and data analysis procedure have also been pointed out.
3.2 Research Design
Research design refers to the way the study is designed, that is the method used to carry
out the research (Mugenda & Mugenda, 2003). Descriptive study was conducted by
collecting quantitative data. Observation which is a method of viewing and recording
data was used.
3.3 Population
The research drew population from all companies listed on the Nairobi Securities
Exchange as at 31st December, 2014. The researcher targeted 44 non-financial firms, as
shown in appendix 2, after excluding 17 companies, which operated in the financial
sector. The financial firms were excluded because their capital structure was controlled
by the regulators.
19
3.4 Data Collection
The study was based on secondary data. The annual financial data for listed firms for
the period 2010-2014 from the Nairobi Securities Exchange, Capital Markets Authority
and respective companies’ websites as well as their official publications. The financial
data collected for each firm was on: Debt (long-term debt and short-term), total equity,
total
revenues, revenues from each segment, total assets, net fixed assets, and net
income.
3.5 Data Analysis
The regression analysis technique was employed to explore the relationship between
diversification and leverage decisions by firms after controlling for some control
variables selected from prior studies that influences the leverage decisions of the firm.
They included firm size (SIZE), profitability (PROF), and tangible assets (TANG).
Diversification was treated as the independent variable in this study. Managers could
control the extent of desired diversification and capital structure was the dependent
variable. Data was categorized, ordered and summarized to obtain answers to the research
question.
3.5.1 Model Specification and Operationalization of Variables
In this study diversification was measured using specialization ratio while leverage was
measured by debt equity ratio. Specialization Ratio (SR) was calculated to classify listed
firms into three, single product firms (SR>0.95), moderate diversified firms (0.75≤ SR <
0.95) and highly diversified firms (SR<0.75). Debt - Equity ratio was used to indicate the
leverage of the firms.
20
The researcher used a multiple regression model to analyze the variables that explain the
determinants of capital structure. The dependent variable in the regression model was
Leverage (LEV) while the independent variables included diversification, size,
profitability, and tangibility.
LEVEit = αi + β1DIVEit + β 2SIZE + β 3PROF + β4TANG + Ɛit
Where:
LEVE
-
means leverage
DIVE
-
means diversification
SIZE
-
means size
PROF
-
means profitability
TANG
-
means tangibility of assets
Β1, β2, β3, β4, as its coefficients which were to be estimated
αi stands for intercept
Ɛt stands for error term
21
Table 3.1 Operationalization of Study Variables
Variable
Indicator
Measure
Adapted From
Leverage
Debt equity ratio
Rajendran &
Madabhushi, 2009
Specialisation ratio
Annual Revenues
(Core segment) to
total revenues
Rumelt,1974
LNTAU – Natural
logarithm of total
assets
Alonso, 2003
Earnings before tax
to total assets
Booth et al.,2000
Net fixed assets to
total assets
Chakraborty, 2010
Dependent
Capital structure
Independent
Diversification
Control Variables
Size
Profitability
Tangibility
3.5.2 Tests of Significance
To examine whether capital structure was significant among the three categories of firms
(single product firms, moderate diversified firms and highly diversified firms) t-test was
used at 5 percent level of significance.
22
CHAPTER FOUR:
DATA ANALYSIS, RESULTS AND DISCUSSION
4.1
Introduction
This chapter details the research findings presented by descriptive statistics and tables.
The regression model and correlation
The
statistics are also presented in this chapter.
study population targeted 44 listed firms, out of which 35 firms (80 percent) as
shown in appendix II, whose complete data was available were studied. The data was
analyzed to answer the research question which was to establish the relationship between
corporate diversification and capital structure of firms listed at the Nairobi Securities
Exchange.
4.2
Descriptive Statistics
The table one below shows descriptive statistics on leverage, diversification, size,
profitability and tangibility that were the study variables.
Table 4.1: Descriptive Statistics on Leverage, Diversification, Size, Profitability and
Tangibility
Leverage
Diversific Size
ation
Profitability Tangibility
N
Statistic
175
175
175
175
175
Range
Statistic
4.27
.91
7.44
1.34
.96
Minimum
Statistic
.00
.09
11.90
-.63
.03
Maximum
Statistic
4.27
1.00
19.34
.71
.99
Mean
Statistic
.61
.97
15.72
.11
.63
Std.
Deviation
Statistic
.77
.077
1.53
.16
.26
23
4.2.1 Leverage
In the 5 year study period, the maximum debt equity ratio was at 4.27 and the minimum
was at zero. Some firms had zero debt equity ratios in 2010 and 2011 meaning that the
firms` total assets were being financed by shareholders` capital. The standard deviation of
leverage as per table one, was at 0.77 indicating a small variation from the mean of
0.61.Over the 5 year study period, firms with debt equity ratio of more than 0.5 on
average were represented by 32 percent. This meant that 68 percent of firms listed in the
NSE had little debt in their capital structure with some being wholly financed by equity.
A majority of the firms had a declining trend with leverage over the five years. One firm,
for example, began with a debt equity ratio of 0.44 in 2010 and ended with a debt equity
ratio of 0.30 in 2014.The firm was generally a focused firm with a diversification ratio of
1.Some firms exemplified the positive theoretical relationship expected between leverage
and diversification. One such firm began with 0.08 leverage in 2010 and ended with 0.73
in 2014. Diversification for the firm was also on the increase over the five years. Some
firms had relatively stable debt equity ratio of more than 1 in the 5 year period. These
firms were highly leveraged.
4.2.2 Diversification
Diversification which was measured using specialization ratio showed that on average a
majority of firms listed at the NSE were diversified. Single product model firms had a
maximum specialization ratio of 1 with the highly diversified firms having a value of
0.09. The most diversified firm over the five year period had a specialization ratio of
0.09. Several firms had their specialization ratio in the 5 year period being 1 indicating
that these firms were highly focused in their core activities.
24
In 2010, a total of twenty three firms (66 percent) had a specialization ratio of 1, which
would indicate that only 34 percent of the firms were diversified. However, in the year
2014, only eight firms (23 percent) were still relying on single products. A total of twenty
seven firms representing 77 percent had diversified. The most diversified firm in 2010
and 2011 had a ratio 0.9 and 0.91 respectively. In 2012, the level of diversification
increased with the most diversified firm having a ratio of 0.86. The diversification trend
continued until 2014 with the highly diversified firm having a ratio of 0.09.
4.2.3 Size
To measure firm size, natural logarithm of total assets was used. In the five year study
period, the average firm size was 15. Firms with a size of 17 and above formed a small
percentage of 14 percent from year 2010 to 2012, 25 percent and 20 percent in 2013 and
2014 respectively.
These big firms had on average a specialization ratio of 1 meaning that the firms were
highly specialized. In terms of leverage for the firms with a value of 17 and above, only
66 percent of the firms had a leverage ratio of 0.5 and above and 34 percent had a
leverage ratio of less than 0.5.
Small sized firms were generally poorly leveraged over the five year period; a small
number of firms however had a leverage ratio being above 1. This was contrary to the
proposition that small firms are poorly leveraged (Kaijage and Elly, 2014). The smallest
firm had a ratio of 11.65 in year 2014, while the largest firm had a natural logarithm of
19.33. The standard deviation was at 1.56 which portrayed a huge variance from the
mean of 15.67.
25
4.2.4 Profitability
Profitability was measured using net income of a firm and standardized using total assets.
The most profitable firms with a profitability ratio of 0.25 and above accounted for 13
percent. An analysis of the 13 percent most profitable firms showed that only 17 percent
of the firms had leverage of 0.5 and above. This meant that 83 percent of the (13 percent
most profitable firms) relied on very little leverage. Thus the effect of profitability on
leverage is indeterminate. The least leveraged firm had a ratio of 0 which meant that such
a firm was wholly financed by shareholder`s equity.
The findings of the study showed that 92 percent of the 13 percent most profitable firms
had a specialization ratio of 1 and the remainder 8 percent was moderately diversified.
The most profitable firm had a ratio of 0.71 over the five year study period, with the least
profitable firm having a ratio of -0.6. The mean profitable rate over the five year study
period was at 0.11. A majority of the NSE firms were profitable with an average
profitability of 0.11. The standard deviation of profitability was at 0.16 which meant that
only a few firms had huge profitable difference with the mean.
4.2.5 Tangibility
Tangibility was measured by net fixed assets to total assets of a firm. Firms with high
tangibility of assets were expected to have more leverage due to the security provision of
their assets. A majority of firms with high tangibility had high leverage ratios, with a few
having no debt. One firm had a tangibility ratio of 0.98 and 0.88 in 2010 and 2011 with
zero leverage ratio in the two years.
26
This meant that contrary to the expected positive relationship between tangibility and
leverage, this firm did not use its tangibility to determine its leverage and diversification.
Over the five year study period, the most tangible firm had a ratio of 0.99 while the least
tangible firm had a ratio of 0.03. The least tangible firm had no leverage towards its
capital which could be explained by the fact that low asset tangibility meant the firm was
unattractive to debt holders.
The mean tangibility was at 0.63 while the standard deviation was at 0.26. The standard
deviation indicated a 26 percent difference with the mean tangibility of firms. Therefore,
there existed a huge variation between firms with high tangibility and firms with low
tangibility.
4.3 Diagnostics Tests
Collinearity test is conducted using tolerance and variance inflation factor. Tolerance
measures the percentage of variance in the independent variable that is not accounted for
by the other independent variables. Tolerance values higher than 0.1 are favorable. VIF
indicates the degree to which the standard errors are inflated due to the levels of
collinearity.
Table 4.2a: Multicollinearity Test Profitability, Tangibility and Size with
Diversification
Profitability
Tolerance
.999
VIF
1.001
a. Dependent Variable: Diversification
Collinearity
Statistics
27
Model
1
Tangibility
.918
1.090
Size
.918
1.089
Table 4.2a above, showed that profitability, tangibility and size did not have any
multicollinearity with diversification. The tolerance levels for profitability, tangibility
and size were all 0.9 and above. This meant that only a small percentage of profitability
and size could be accounted for by diversification.
Table 4.2b : Multicollinearity Test Profitability, Tangibility and Diversification
with Size
Model
1
Profitability
Collinearity
Statistics
Tolerance
VIF
Tangibility
Diversification
.996
.996
.993
1.004
1.004
1.007
a. Dependent Variable: Size
From table 4.2b above, there were no multicollinearity issues between profitability,
tangibility, and diversification with size. The degrees of inflation of the standard errors of
profitability, tangibility and diversification were considerably low, with large tolerance
values for the three variables.
Table 4.2c : Multicollinearity Test Profitability, Tangibility and Diversification
with Tangibility
Model
1
Profitability
Collinearity
Tolerance
Statistics
VIF
Size
Diversification
.997
.999
.996
1.003
1.001
1.005
a. Dependent Variable: Tangibility
28
Table 4.2c above indicated that profitability, size and diversification did not have any
multicollinearity with tangibility. The VIF ratios were less than three meaning
multicollinearity between variables did not exist. The tolerance level for profitability, size
and diversification were high above 0.9 meaning only a small percentage of their
variations could be explained by tangibility.
Table 4.2d : Multicollinearity Test Size, Diversification and Tangibility with
Profitability
.918
Model
1
Diversification
.996
Tangibility
.916
1.089
1.004
1.092
Size
Collinearity
Tolerance
Statistics
VIF
a. Dependent Variable: Profitability
Table 4.2d also showed the inexistence of multicollinearity between size, diversification,
and tangibility with profitability. The tolerance values for size, diversification and
tangibility were all above 0.1 meaning that only a small percentage of the variables could
be explained by profitability.
Table 4.3 Durbin Watson`s Test: Model Summary
Model
1
R
.303a
R Square
Adjusted R
Std. Error of the
Durbin-
Square
Estimate
Watson
.092
.070
.74342
a. Predictors: (Constant), Tangibility, Profitability, Diversification, Size
b. Dependent Variable: Leverage
29
.881
Table 4.3 above, shows the Durbin Watson`s test which normally checks if there is
autocorrelation in the residuals from a statistical regression analysis. The Durbin Watson
value was 0.881 which indicated the presence of a weak positive autocorrelation in the
sample. The adjusted R2 indicated that only 7 percent of the variations in the dependent
variable could be explained by the variations in the independent variables.
Table 4.4a Tests of Normality :Case Processing Summary
Leverage
Cases
Missing
N
Percent
0
0.0%
Valid
N
Percent
175 100.0%
Total
N
Percent
175 100.0%
Diversification
175
100.0%
0
0.0%
175
100.0%
Size
175
100.0%
0
0.0%
175
100.0%
Profitability
175
100.0%
0
0.0%
175
100.0%
Tangibility
175
100.0%
0
0.0%
175
100.0%
The table 4.5a above serves to show that there was no variable that was left out in
carrying the normality tests.
Table 4.4b Tests of Normality
Leverage
Kolmogorov-Smirnova
Statistic
Df
Sig.
.255
175
.000
Shapiro-Wilk
Statistic
df
.723
175
Sig.
.000
Diversification
.370
175
.000
.310
175
.000
Size
.067
175
.054
.990
175
.241
Profitability
.154
175
.000
.870
175
.000
Tangibility
.245
175
.000
.416
175
.000
a. Lilliefors Significance Correction
30
From table 4.4b above, leverage, diversification, profitability and tangibility were
significant with sigma`s of 0.000 which were less than 0.05. The data for leverage,
diversification, profitability and tangibility was not normally distributed. Size had a
sigma of 0.54 using Kolmogorov test and a 0.24 sigma using Shapiro-Wilk test. Size was
not statistically significant. Data from size as a variable was normally distributed.
4.4 Relationship between Study Variables
In statistics it is generally accepted that the following can be used to measure effect size.
Effect size: if v = +/-0.5 it is large, +/-0.3 it is medium, and +/-0.1 it is small.
Table 4.5: Correlation Matrix on Relationship between Study Variables
Leverage
Diversification Size
Profitability
Tangibility
Pearson
Leverage
1
correlation
Diversificati
Pearson
on
correlation
Pearson
Size
0.025
1
.185*
-.037
1
-.210**
.056
.005
1
.144
-.059
.286**
.027
correlation
Pearson
Profitability
correlation
Pearson
Tangibility
correlation
*. Correlation is significant at the 0.05 level (2-tailed).
**. Correlation is significant at the 0.01 level (2-tailed).
31
1
For the 5 year study period, leverage was positively correlated with diversification, size
and tangibility of assets with scores of r as follows r=0.025; r=0.185 and r=0.144
respectively. Leverage had a weak positive association with size but statistically
significant at 95 percent level of confidence. There was also a weak negative association
between leverage and profitability represented by r = -0.210, the relationship however
was statistically significant at 95 percent level of confidence.
Diversification had a weak positive association with profitability and leverage with an r
value of 0.056 and 0.25 respectively. It was however, negatively correlated with size and
tangibility at -0.037 and -0.059 respectively. The effect of size, tangibility, profitability
and leverage on diversification was small as the values were both below +/-0.3. This
meant that an increase in diversification of a firm led to a small effect increase in a firm`s
profitability and leverage. An increase in diversification however led to a small
decreasing effect on a firm`s size and tangibility of assets.
Firm size was positively correlated to a firm`s profitability, tangibility and leverage. The
respective Pearson’s r correlations were at 0.005, 0.286 and 0.185. There was a weak
negative association between firm size and diversification with an r of -0.37. Leverage
had a weak positive relationship with size which was statistically significant at 95 percent
level of confidence.
32
Tangibility on the other hand, had a weak positive association with size which was
statistically significant at 95 percent level of confidence. Firm size had a small effect on a
firm`s profitability, tangibility and leverage, however it had a medium effect on
diversification.
A weak positive association existed between profitability and tangibility, profitability and
diversification, profitability and size at r=0.27; r=0.056 and r=0.005 respectively. The r
value of profitability with leverage was -0.210 which meant that profitability caused a
small negative effect on leverage. The relationship between profitability and leverage was
however significant at 95 percent level of confidence. Profitability had an r value of
0.005 with size. Profitability thus had a small effect on firm size.
Tangibility of assets correlated positively with leverage, size and profitability at r=0.144,
r=0.286 and r=0.27 respectively. Tangibility had a weak positive association with
leverage, size and profitability. The relationship between tangibility and size was
however significant r=0.286, at 95 percent level of confidence. A weak negative
association existed between tangibility and diversification with r=-0.059. An increase in a
firm`s tangible assets meant a small decline in a firm`s diversification.
4.5 Corporate Diversification and Capital Structure
In the study a multiple regression model was used to predict the relationship between
capital structure and the hypothesized factors determining it for NSE firms listed in
Kenya. The adjusted (R2) value was 0.07; meaning that 7 percent of variations in leverage
for NSE listed firms was explained by variations in the model’s independent variable and
control variables. This was summarized in the following Table 3 below.
33
Table 4.6a Regression statistics: Model Summary
Model
R
R Square
Adjusted R Square
Std. Error of the
Estimate
.303a
1
.092
.070
.74342
a. Predictors: (Constant), Tangibility, Profitability, Diversification, Size
Table 4.6b : ANOVAa
Model
Sum of Squares Df
Regression
1
Residual
Total
Mean Square F
9.467
4
2.367
93.954
170
.553
103.421
174
Sig.
.003b
4.282
a. Dependent Variable: Leverage
b. Predictors: (Constant), Tangibility, Profitability, Diversification, Size
Table 4.6c: Regression Coefficientsa
Model
Unstandardized
Standardized
Coefficients
Coefficients
B
Beta
Std.
T
P
95.0%
Value
Interval for B
Error
(Constant)
Diversification
1Size
Profitability
Tangibility
-1.203
.939
.494
.738
.079
.038
-1.021
.345
.325
.231
Confidence
Lower
Upper
Bound
Bound
-1.281
.202
-3.056
.650
.049
.669
.504
-.962
1.949
.158
2.065
.040
.003
.155
-.217 -2.958
.004
-1.702
-.340
.160
-.130
.780
.108
a. Dependent Variable: Leverage
34
1.411
From the regression table 3a to 3c above, the study model would translate to;
LEVEit = -1.203+0.049 DIVE+0.158 SIZE-0.217 PROF+0.108 TANG+0.74
Where:
LEVE=Leverage
DIVE=Diversification
SIZE=Size
PROF=Profitability
TANG=Tangibility
From the regression coefficients in Table 3c above, the constant for the leverage model
was -1.203 given that all other factors were held constant and the error term was 0.939.
Diversification, size and tangibility had positive beta coefficients which meant that an
increase in any of the variables led to a corresponding increase in leverage. The
profitability variable had a negative coefficient of -0.217. This meant that profitability
was inversely correlated to leverage for the NSE listed firms. Any increase in profitability
led to a negative increase in leverage.
At 5 percent level of significance or 95 percent level of confidence; P=0.05. Profitability
and size were statistically significant with leverage at P values of 0.004 and 0.04
respectively. Their P values were less than 0.05. Diversification and tangibility however,
had no statistical significance with leverage as shown by their P values of 0.5 and 0.16
respectively.
35
From the above findings, leverage was on the decline for most firms from 2010 to 2014.
Out of the 35 firms studied, 52 percent employed less debt over the 5 year study period
and were more diversified; 11 percent of the firms used more debt in their capital
structure and diversified further. A small number which formed 17 percent took up more
debt but remained single product firms while 20 percent used less debt and still remained
focused firms. Thus a majority of the NSE firms that were diversified increased their
leverage.
36
CHAPTER FIVE:
SUMMARY, CONCLUSIONS AND RECOMMENDATIONS
5.1 Introduction
This chapter presents the summary, the highlights of the study findings and the
recommendations thereof. The conclusions were in quest to addressing the research
objective of establishing the effect of diversification on capital structure of firms listed at
the NSE. It also highlights the limitations of the study and suggestions for further
research.
5.2 Summary of Findings
By use of descriptive statistics, the research sought to establish the relationship between
corporate diversification and capital structure of firms listed in the Nairobi Securities
Exchange. The study involved 35 firms whose data was collected from the year 2010 to
2014. The data was mainly from annual financial reports of respective companies. The
annual reports were obtained from the firms’ websites, Capital Markets Authority and
other relevant publications.
Diversification of NSE firms for the period 2010 – 2014, had a weak positive relationship
with leverage with a beta coefficient of 0.049 and a Pearson`s correlation r of 0.025. This
was inconsistent with the co-insurance effect theory which argued that a firm with
imperfectly correlated cash flow would tend to assume more debt in its capital structure.
37
According to the study, 52 percent of the NSE firms used less debt and were more
diversified with only 11 percent of the firms taking up more debt for diversification
purposes. Firms therefore choose to use other internal sources of capital consistent with
Myers (1984) pecking order theory where firms prefer to use internal sources of funds
before external finance.
Firm size and leverage had a weak positive relationship with a beta coefficient of 0.158
and a correlation matrix of 0.185. From the study, only 11 percent of NSE firms used
more debt to diversify. Out of the 11 percent, 1 percent was represented by small sized
firms whilst the 99 percent were large firms. This is consistent with the trade off theory
by Sbeiti (2010) which suggests a positive relationship between size and leverage. Thus
the bigger the firm the lower the financial distress risks compared to small sized firms.
Profitability had a weak negative association with leverage with a beta of -0.217. The
relationship between profitability and leverage was significant at r =-0.210. This means
that an increase in profitability led to a decrease in leverage. Most scholars found the
relationship between profitability and leverage as being indeterminate. According to
Myers (1984) firms prefer least sensitive and least risky sources of capital before using
most sensitive and more risky funds. Profitable firms have access to retained profits and
could rely on them as opposed to debt.
38
Tangibility on the hand had a weak positive relationship with leverage with a beta
coefficient of 0.108. This meant that an increase in tangible assets led to a small increase
in a firm`s leverage. Firm size and tangibility of assets had a positive relationship which
was significant at 95 percent confidence level, with an r=0.286. Large firms with a
natural logarithm of total assets of 17 and above, for the 5 year study period, had high
leverage usage with 66 percent having a leverage of more than 0.5.
Analyses show that a majority of the large firms were highly specialized and performed
better than diversified firms. This could imply that management of firms that develop
through diversification are not very effective at utilizing their assets to generate profit or
may not be efficiently utilizing shareholder`s funds. The finding supports the proposition
of Peter and Waterman (1982) that successful companies are those that stick to the
knitting; meaning firms that choose to do what they are good at and concentrate on
perfecting it.
Multiple regression and bivariate correlation were used to analyze the data.
Diversification was measured using Specialization Ratio, calculated as a ratio
of
annual revenue from the core segment of a firm to its total annual revenue. The
lowest ratio was 0.09 indicating high level of diversification, as the core business of the
firm contributed to only 9 percent of its total revenue. The study showed that in 2010, 34
percent of NSE firms were diversified; the percentage however rose to 77 percent by
2014. Firms with a specialization ratio of 1 were highly focused firms with a percentage
of 66 percent in 2010 and 23 percent in 2014.
39
A test of multicollinearity between the study independent variables was carried out
which showed the inexistence of multicollinearity amongst diversification, size,
profitability and tangibility. The normality tests that were carried out showed that only
size had a normal distribution. Data for leverage, diversification, profitability and
tangibility did not follow a continuum hence there was no normal distribution for the
variables.
5.3 Conclusions
The research findings showed that on average firms listed at the NSE were diversified.
There existed a weak positive relationship between diversification and capital structure of
NSE listed firms. The effect of diversification had an adjusted R2 of 0.07. This meant that
7 percent of a firm’s leverage could be explained by diversification and the control
variables in the study. Thus, other than diversification, there were other factors that had a
greater impact on a firm`s capital structure.
Firm size and tangibility of assets were positively related with diversification. The bigger
a firm was the more tangible assets it had and the better chances it had of being
leveraged. The smaller a firm was, the fewer tangible assets it had and the less chances it
had of being leveraged. This was consistent with Titman and Wessels (1988) who argued
that small firms are likely to worsen the information asymmetry between small firm
shareholders and managers, thus these firms were viewed as risky by investors.
40
Profitability was the only variable that had a weak negative relationship with leverage.
This is because shareholders` of a firm evaluate the various sources of capital before they
diversify. Decisions of whether to diversify or specialize and utilize leverage are not
based on profitability alone. Firm owners have the discretion of making capital financing
decisions.
According to Kaijage and Elly (2014), profitable small and medium term enterprises tend
to use retentions as their principle sources of funds contrary to the cheap sources of funds
such as debt. This demonstrates the indeterminate nature of profitability of firms on a
firm`s diversification strategy.
5.4 Recommendations
Company financing decisions involve a lot of policy issues. The results of the study have
significant policy implications on the firm, industry and macro levels. The study found
out that more of NSE firms used less leverage for diversification purposes. Thus the
government should regulate the financial sector through various monetary and fiscal
policies to reduce the cost of borrowing. The high interest rate in Kenya is an impediment
to the growth and diversification of firms.
The study found out that large firms were more leveraged than small - sized firms due to
the stability and less financial distress risk associated with large firms. Firm size was
however positively related with long-term debt and negatively related with short term
debt.
41
Thus to improve the leverage of small sized firms, managers should concentrate on using
more of current liabilities to finance assets. The CMA should create redeemable shortterm financing products in addition to corporate bonds to be traded in the stock market.
This will be beneficial to small sized firms as they will be able to access finance easily
and diversify.
In theory firms that used diversification to develop, were expected to be have more
leverage in their capital structure than non-diversified firms. This was in accordance to
the co-insurance effect generated by the imperfectly correlated cash flows. In practice
most firms used less debt to diversify. The CMA should come up with policies that
cheapen the external sources of finance for firms to access capital easily and reduce the
information asymmetries between shareholders and managers of firms.
5.5 Limitations of the Study
From the study, one cannot conclude that corporate diversification affects leverage
beyond the commonly accepted capital structure determinants. The insignificant results
obtained may be attributed to limited observation sample used in the study as a
consequence of the difficulty of finding data on corporate diversification for NSE listed
firms. The study did not focus on the various effects of diversification strategies; related
and unrelated, on capital structure but rather looked at the effect of diversification in
totality. There was also an assumption that corporate diversification and capital structure
had a linear relationship which may not necessarily be the case.
42
The study was based on secondary data collected from audited financial statements and
respective firm websites`. Thus if there are any misstatements or errors arising from the
financial statements, then the study is limited to such errors. The study looked at four
elements that influence capital structure decisions; however, there are more factors that
affect the gearing level of a firm which would need to be studied in future.
5.6 Suggestions for further Research
La Rocca et.al. (2009) suggested an interesting direction for future empirical studies
would be to study the combined effect of international and product diversification,
according to the degree of relatedness of product segments on capital structure decisions.
Future research could measure corporate diversification in a different manner. The use of
Entropy Index (EI) in measuring diversification could be valuable in drawing more
meaningful and comprehensive results.
A study on the diversification strategies both related and unrelated should be carried out
to find out the impact of these strategies on the capital structure of a firm. Other
determinants of capital structure such as firm performance and non-debt tax shields
should be studied to find out if they have significant effect on capital structure decisions
of firms listed in the NSE.
43
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its
APPENDICES:
APPENDIX 1: DATA COLLECTION SHEET
No. Company
1
KAKUZI
2
KAPCHORUA
3
LIMURU TEA
4
REA VIPINGO
PLANTATIONS
5
SASINI
6
WILLIAMSON TEA
7
CAR & GENERAL
CAR & GENERAL
Year
2010
2011
2012
2013
2014
2010
2011
2012
2013
2014
2010
2011
2012
2013
2014
Leve
0.28247
0.25733
0.22292
0.22945
0.23295
0.32560
0.40118
0.32847
0.33210
0.31534
0.23282
0.24077
0.27752
0.28593
0.28029
Dive
0.99279
0.98034
0.94993
0.94124
0.94880
1.00000
1.00000
1.00000
0.99814
0.98308
1.00000
1.00000
1.00000
1.00000
1.00000
Size
14.85752
15.05856
15.04681
15.08818
15.11843
13.89738
14.07488
14.22497
14.38867
14.43236
11.89893
12.13218
12.64267
12.22747
12.23851
Prof
0.19705
0.26545
0.16575
0.06703
0.06326
0.11014
0.20708
0.07475
0.06198
0.03678
0.70919
0.32219
0.47376
0.20338
0.01006
Tang
0.85471
0.76242
0.68139
0.71334
0.72727
0.75570
0.76711
0.80392
0.76622
0.71729
0.46957
0.48937
0.61153
0.63850
0.55992
2010
2011
2012
2013
2014
2010
2011
2012
2013
2014
2010
2011
2012
2013
2014
2010
2011
2012
2013
2014
0.28417
0.26867
0.22733
0.84291
0.91708
0.31603
0.31298
0.29728
0.41853
0.23171
0.26213
0.25148
0.25904
0.24358
0.23886
1.02543
1.03114
1.01246
1.75597
1.32983
1.00000
1.00000
1.00000
0.95364
0.94640
1.00000
1.00000
1.00000
0.95264
0.09198
1.00000
1.00000
1.00000
1.00000
1.00000
0.99828
0.99712
0.99504
0.99809
0.99298
14.05466
14.43797
14.56628
14.65908
14.72570
15.96039
15.99915
15.93626
16.01876
16.51885
15.29261
15.49174
15.64425
15.80136
15.92170
15.16910
15.53151
15.55692
15.74724
15.91387
0.08181
0.36428
0.26210
0.28019
0.21362
0.11635
0.05072
-0.01489
0.01750
0.00414
0.27927
0.24202
0.18688
0.15864
0.00877
0.08503
0.07693
0.06214
0.06650
0.05155
0.88219
0.74837
0.70662
0.62015
0.61694
0.91703
0.92569
0.93712
0.85697
0.91660
0.77602
0.69331
0.77032
0.73292
0.70827
0.30825
0.37292
0.40457
0.39308
0.38352
51
8
CMC HOLDINGS
9
MASHALLS
10
EXPRESS KENYA
11
KENYA AIRWAYS
12
LONGHORN
13
NATION
GROUP
14
SCAN GROUP
15
2010
2011
2012
2013
2014
2010
2011
2012
2013
2014
2010
2011
2012
2013
2014
2010
2011
2012
2013
2014
2010
2011
2012
2013
2014
0.07778
0.08384
0.11847
1.10679
0.72912
3.19325
0.00000
0.00128
0.82600
1.15985
1.03391
1.30119
0.68502
1.42059
1.65206
1.63771
1.44260
1.33141
2.93060
4.26611
0.07647
0.02389
0.00000
0.83837
0.83021
0.98716
0.98151
0.98365
0.99119
0.96485
1.00000
1.00000
1.00000
0.93807
0.90824
1.00000
1.00000
1.00000
0.85815
0.84455
1.00000
1.00000
1.00000
1.00000
1.00000
0.99108
0.99721
0.98759
0.99334
0.99550
16.37716
16.49510
16.50116
16.32497
16.60019
13.64369
13.70341
12.88062
13.15215
13.31122
13.96618
13.35210
12.97531
13.08263
13.07720
17.83014
17.85026
17.83368
18.62501
18.81715
13.16734
13.47253
13.40253
13.43720
13.52453
0.03139
-0.01243
0.00718
0.01625
0.01967
-0.40934
0.20303
-0.42159
-0.21360
-0.00411
-0.02416
-0.36413
0.03018
-0.00353
-0.15993
0.04821
0.08849
0.03860
0.08825
0.03270
0.05240
0.30025
-0.03922
0.22091
0.19695
0.18852
0.15573
0.19769
0.23652
0.21526
0.65984
0.45144
0.94235
0.71420
0.69974
0.67241
0.56795
0.77410
0.78524
0.84302
0.95091
0.97509
0.96541
0.76679
0.80064
0.27353
0.25748
0.03289
0.29298
0.26582
2010
2011
2012
2013
2014
2010
2011
2012
2013
2014
0.00000
0.02662
0.01873
0.01024
0.00660
0.05342
0.07748
0.07308
0.04410
0.03529
1.00000
1.00000
1.00000
0.80431
0.80991
0.98483
0.98718
0.98725
0.98258
0.98467
15.50599
15.65374
15.82516
16.25299
16.29576
15.89613
15.95439
15.97272
16.36062
16.40208
0.39590
0.31928
0.46974
0.31344
0.30341
0.10468
0.15078
0.12664
0.04908
0.07250
0.53455
0.47112
0.45964
0.31244
0.61745
0.11131
0.08445
0.10540
0.17926
0.17773
STANDARD MEDIA
GROUP
2010
2011
2012
2013
2014
0.44564
0.40124
0.31508
0.32260
0.30378
1.00000
1.00000
1.00000
1.00000
1.00000
14.63543
14.65610
14.68381
15.24162
15.22692
0.19982
0.10014
0.11136
0.07224
0.07950
0.85305
0.95980
0.94562
0.60514
0.63649
MEDIA
52
16
TPS EASTERN
AFRICA
17
UCHUMI HOLDINGS
18
ARM CEMENT
19
BAMBURI CEMENT
20
CROWN PAINTS
KENYA
21
EA CABLES
22
E.A. PORTLANDS
CEMENT
23
KENGEN LTD
2010
2011
2012
2013
2014
2010
2011
2012
2013
2014
2010
2011
2012
2013
2014
2010
2011
2012
2013
2014
0.36935
0.43119
0.39806
0.28059
0.26468
0.20803
0.08045
0.03022
0.06837
0.05283
1.71135
1.63758
1.87202
1.51101
1.21614
0.19495
0.05092
1.67396
0.17534
0.17044
1.00000
1.00000
1.00000
0.93524
0.95643
1.00000
1.00000
1.00000
0.94040
0.91623
1.00000
1.00000
1.00000
0.98162
0.96541
0.97788
0.99877
0.86542
0.98625
0.99041
16.14427
16.25930
16.25930
16.41833
16.44488
14.90055
15.00959
15.02380
15.53354
15.74483
16.40766
16.63646
16.83351
17.20683
17.42406
17.32125
17.32712
17.57759
17.42737
17.34428
0.06750
0.07408
0.06265
0.05597
0.01587
0.14637
0.15599
0.12048
0.08718
0.06576
0.08332
0.08116
0.08754
0.06733
0.05467
0.22711
0.25270
0.16674
0.14894
0.17021
0.93395
0.93057
0.99021
0.84461
0.94976
0.62816
0.62491
0.75816
0.69045
0.67313
0.92263
0.95850
0.92990
0.76945
0.77770
0.61379
0.60134
0.61750
0.72839
0.74661
2010
2011
2012
2013
2014
2010
2011
2012
2013
2014
0.08668
0.08640
0.04026
0.01096
0.00366
0.38854
0.28361
0.27056
0.32500
0.48641
0.97107
0.95544
0.96881
0.98403
0.97665
1.00000
1.00000
1.00000
0.99012
0.98260
14.49473
14.56945
14.63011
14.89577
15.16431
14.95305
14.88666
15.12827
15.73831
15.88104
0.08593
0.09436
0.09927
0.11321
0.03932
0.08292
0.15923
0.20268
0.08558
0.06432
0.24959
0.30397
0.29625
0.02617
0.25596
0.87294
0.88584
0.86567
0.47165
0.51242
2010
2011
2012
2013
2014
2010
2011
2012
2013
0.78926
1.00436
1.44154
0.74383
0.82037
1.03595
1.15701
1.11067
1.31212
1.00000
1.00000
1.00000
0.98901
0.97113
1.00000
1.00000
1.00000
0.93909
16.30354
16.42048
16.46105
16.59642
16.57027
18.78252
18.82439
18.81369
19.05553
-0.02813
-0.00880
-0.06030
-0.08798
0.02378
0.01731
0.02438
0.02731
0.02134
0.75812
0.76557
0.81758
0.77674
0.78851
0.81978
0.94469
0.95081
0.86682
53
24
KENOL
LIMITED
25
TOTAL KENYA
26
CENTUM
27
TRANSCENTURY
LIMITED
28
BOC
29
30
31
2014
1.93326
0.93201 19.33779 0.01662
0.88957
2010
2011
2012
2013
2014
2010
2011
0.02536
0.13130
0.13926
0.10735
0.03898
0.38674
0.32851
1.00000
1.00000
1.00000
0.98698
0.98882
1.00000
1.00000
16.25729
16.39422
16.46479
17.15205
16.99002
16.40213
16.31821
0.24677
0.37434
-0.63382
0.02005
0.06359
0.10451
0.00474
0.37930
0.44221
0.51919
0.31079
0.35238
0.77240
0.80711
2012
2013
2014
2010
2011
2012
2013
2014
0.06023
0.07263
0.07246
0.00000
0.00000
0.09959
0.38630
0.28981
1.00000
0.99674
0.99681
1.00000
1.00000
1.00000
1.00000
1.00000
16.52672
17.50399
17.29804
15.87681
16.07303
16.23225
16.75795
16.84066
-0.00427
0.05213
0.06994
0.13757
0.24002
0.12192
0.17129
0.19476
0.64012
0.24877
0.30790
0.98509
0.88914
0.98502
0.76589
0.81621
2010
2011
2012
2013
2014
2010
2011
2012
2013
2014
0.63692
1.21608
1.13498
0.05013
0.12865
0.32761
0.36751
0.36756
0.26831
0.31658
0.89720
0.91348
0.90642
0.90308
0.96859
0.97345
0.97855
0.95802
0.86433
0.94082
16.23468
16.92565
16.89952
16.98689
16.78406
14.51851
14.41259
14.50341
14.78367
14.64856
0.05612
0.03876
0.05614
0.03601
-0.10862
0.05678
0.11831
0.14410
0.11712
0.12085
0.63559
0.57812
0.65624
0.63154
0.57692
0.36586
0.35513
0.30254
0.53989
0.48566
0.37162
0.31158
0.28542
0.22940
0.22013
0.99122
0.98974
0.98620
1.00000
1.00000
16.22440
16.43659
16.53526
16.13839
16.21975
0.24480
0.32610
0.31327
0.53601
0.55059
0.56802
0.49240
0.53021
0.82979
0.83839
0.11737
0.15465
0.12699
0.04594
0.07221
1.00000
1.00000
1.00000
0.89249
0.87587
14.18404
14.34277
14.43751
14.60597
14.74498
0.30302
0.22087
0.28746
0.28783
0.23578
0.77964
0.78846
0.73738
0.59532
0.61286
0.11440
0.98367 17.09394 0.47366
0.78614
KOBIL
BRITISH AMERICAN
TOBACCO
2010
2011
2012
2013
2014
CARBACID
INVESTMENTS LTD 2010
2011
2012
2013
2014
EAST
AFRICAN
BREWERIES
2010
54
32
EVEREADY
33
MUMIAS SUGAR
34
UNGA GROUP
35
SAFARICOM
LIMITED
2011
2012
2013
2014
2010
2011
2012
2013
2014
2010
0.27116
2.68299
3.50155
3.01737
1.96437
2.63955
2.29260
1.00000
1.00000
3.71344
0.98935
0.88567
1.00000
1.00000
0.99731
0.99643
0.99141
1.00000
1.00000
1.00000
17.34217
17.41355
17.48233
17.57808
13.99435
13.83228
13.95591
13.75555
13.74300
16.51228
0.36045
0.41759
0.28407
0.24168
0.01233
-0.17033
0.05989
0.06383
0.26666
0.14697
0.97615
0.87883
0.79519
0.82225
0.21109
0.27849
0.23871
0.27376
0.17924
0.78116
2011
2012
2013
2014
2010
2011
2012
2013
2014
0.39644
0.37878
0.41029
0.21483
0.50516
0.52443
0.60690
1.07102
0.46347
1.00000
1.00000
0.94983
0.95312
1.00000
1.00000
1.00000
1.00000
0.96114
16.80953
16.89188
17.12174
16.97519
15.43775
15.55754
15.67341
15.93380
15.89827
0.13256
0.08137
-0.08147
-0.14451
0.04663
0.07726
0.05432
0.07963
0.07073
0.82220
0.93308
0.74122
0.81525
0.32473
0.02843
0.27540
0.29833
0.31662
2010
2011
2012
2013
2014
0.12801
0.18102
0.20357
0.14950
0.05593
1.00000
1.00000
1.00000
0.97637
0.98786
18.21673
18.33897
18.42771
18.45508
18.48158
0.25710
0.19925
0.17248
0.24590
0.32917
0.86206
0.86526
0.83693
0.89145
0.90646
55
APPENDIX 2: LIST OF LISTED NON- FINANCIAL FIRMS AS AT DEC 2014
1. Eaagads Ltd
23. Crown Paints Kenya Ltd
2. Kakuzi Ltd
24. E.A.Cables Ltd
3. Kapchorua Tea Co. Ltd
25. E.A.Portland Cement Co. Ltd
4. The Limuru Tea Co. Ltd
26. KenGen Co. Ltd
5. Rea Vipingo Plantations Ltd
27. KenolKobil Ltd
6. Sasini Ltd
28. Kenya Power & Lighting Co Ltd
7. Williamson Tea Kenya Ltd
29. Total Kenya Ltd
8. Car & General (K) Ltd
30. Umeme Ltd
9. CMC Holdings Ltd
31. Centum Investment Co Ltd
10. Marshalls (E.A.) Ltd
32. Olympia Capital Holdings Ltd
11. Sameer Africa Ltd
33. Trans-Century Ltd
12. Express Kenya Ltd
34. A.Baumann & Co Ltd
13. Hutchings Biemer Ltd
35. B.O.C Kenya Ltd
14. Kenya Airways Ltd
36. British American Tobacco Kenya
15. Longhorn Kenya Ltd
37. Carbacid Investments Ltd
16. Nation Media Group Ltd
38. East African Breweries Ltd
17. Scangroup Ltd
39. Eveready East Africa Ltd
18. Standard Group Ltd
40. Kenya Orchards Ltd
19. TPS Eastern Africa Ltd
41. Mumias Sugar Co. Ltd
20. Uchumi Supermarket Ltd
42. Unga Group Ltd
21. ARM Cement Ltd
43. Safaricom Ltd
22. Bamburi Cement Ltd
44. Home Afrika Ltd
56